Apache Spark

Overview

When your data outgrows a single machine, we turn to Apache Spark. Our engineers build distributed data processing pipelines that handle terabytes to petabytes of data — using Spark SQL for analytics, MLlib for machine learning, and Structured Streaming for real-time workloads.

Our Capabilities

• Distributed data processing at scale
• Spark SQL for interactive analytics
• MLlib for large-scale machine learning
• Structured Streaming for real-time data
• Delta Lake for reliable data lakes
• PySpark & Scala API development
• Cluster optimization & performance tuning
• Integration with Kafka, S3, HDFS & more

Common Use Cases

• Petabyte-scale ETL pipelines
• Real-time fraud detection
• Log analytics & anomaly detection
• Large-scale feature engineering